Method and apparatus for detection of nervous system disorders

ABSTRACT

Systems and methods for detecting and/or treating nervous system disorders, such as seizures, are disclosed. Certain embodiments of the invention relate generally to implantable medical devices (IMDs) adapted to detect and treat nervous system disorders in patients with an IMD. Certain embodiments of the invention include detection of seizures based upon comparisons of long-term and short-term representations of physiological signals. Other embodiments include prediction of seizure activity based upon analysis of physiological signal levels. An embodiment of the invention monitors the quality of physiological signals, and may be able to compensate for signals of low signal quality. A further embodiment of the invention includes detection of seizure activity following the delivery of therapy.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 60/793,869, filed on Apr. 21, 2006, the contents of which areincorporated by reference.

FIELD OF THE INVENTION

The present invention relates generally to implantable medical devices(IMDs), and more particularly relates to systems and methods fordetecting and/or treating nervous system disorders, such as seizures, ina patient with an IMD.

BACKGROUND OF THE INVENTION

Nervous system disorders affect millions of people, causing adegradation of life, and in some cases, death. Nervous system disordersinclude disorders of the central nervous system, peripheral nervoussystem, and mental health and psychiatric disorders. Such disordersinclude, for example without limitation, epilepsy, Parkinson's disease,essential tremor, dystonia, and multiple sclerosis (MS). Additionally,nervous system disorders include mental health disorders and psychiatricdisorders which also affect millions of individuals and include, but arenot limited to, anxiety (such as general anxiety disorder, panicdisorder, phobias, post traumatic stress disorder (PTSD), and obsessivecompulsive disorder (OCD)), mood disorders (such as major depression,bipolar depression, and dysthymic disorder), sleep disorders (e.g.,narcolepsy), obesity, and anorexia.

As an example, epilepsy is a serious neurological disease prevalentacross all ages. Epilepsy is a group of neurological conditions in whicha person has or is predisposed to recurrent seizures. A seizure is aclinical manifestation resulting from excessive, hypersynchronous,abnormal electrical or neuronal activity in the brain. A seizure is atype of adverse neurological event that may be indicative of a nervoussystem disorder. This electrical excitability of the brain may belikened to an intermittent electrical overload that manifests withsudden, recurrent, and transient changes of mental function, sensations,perceptions, and/or involuntary body movement. Because the seizures areunpredictable, epilepsy affects a person's employability, psychosociallife, and ability to operate vehicles or power equipment. It is adisorder that occurs in all age groups, socioeconomic classes, cultures,and countries. In developed countries, the age-adjusted incidence ofrecurrent unprovoked seizures ranges from 24/100,000 to 53/100,000person-years and may be even higher in developing countries. Indeveloped countries, age-specific incidence is highest during the firstfew months of life and again after age 70. The age-adjusted prevalenceof epilepsy is 5 to 8 per 1,000 (0.5% to 0.8%) in countries wherestatistics are available. In the United States alone, epilepsy andseizures affect 2.3 million Americans, with approximately 181,000 newcases occurring each year. It is estimated that 10% of Americans willexperience a seizure in their lifetimes, and 3% will develop epilepsy byage 75.

There are various approaches in treating nervous system disorders.Treatment therapies can include any number of possible modalities aloneor in combination including, for example, electrical stimulation,magnetic stimulation, and/or drug infusion. Each of these treatmentmodalities can be operated using closed-loop feedback control. Suchclosed-loop feedback control techniques receive neurological signals(e.g., from a monitoring element) carrying information about a symptomor a condition or a nervous system disorder. Such a neurological signalcan include, for example, electrical signals (such aselectroencephalogram (EEG), electrocorticogram (ECoG), and/orelectrocardiogram (EKG) signals), chemical signals, other biologicalsignals (such as changes in the quantity of neurotransmitters),temperature signals, pressure signals (such as blood pressure,intracranial pressure or cardiac pressure), respiration signals, heartrate signals, pH-level signals, and peripheral nerve signals (such ascuff electrodes placed on a peripheral nerve). Monitoring elements caninclude, for example, recording electrodes or various types of sensors.

For example, U.S. Pat. No. 5,995,868 to Dorfmeister et al., incorporatedherein by reference in relevant part, discloses a system for theprediction, rapid detection, warning, prevention, or control of changesin activity states in the brain of a patient. Use of such a closed-loopfeed back system for treatment of a nervous system disorder may providesignificant advantages. For example, it may be possible for treatment tobe delivered before the onset of the symptoms of the nervous systemdisorder.

In the management of a nervous system disorder, it may be important todetermine an extent of a neurological event, a location of theneurological event, a severity of the neurological event, and theoccurrence of multiple neurological events in order to prescribe and/orprovide a delivery of a treatment or otherwise manage the neurologicaldisorder. A patient, for example, would not benefit from a medicaldevice system if the patient experienced a neurological event but wasnot administered treatment because the medical device system did notdetect the neurological event. On the other hand, a patient may sufferadverse effects, for example, if subjected to a degree of treatmentcorresponding to multiple neurological events, such as seizures, when infact the patient had experienced only one neurological event, or aseries of minor events, or no neurological event at all. As used herein,the term “neurological event” may encompass physiological events, suchas seizures, as well as events defined artificially, for example, bymeasurable signal processing parameters.

Glossary of Terms

The “onset of the clinical component” of a seizure is the earlier ofeither (1) the time at which a patient becomes is aware that a seizureis beginning (the “aura”), or (2) the time at which an observerrecognizes a significant physical or behavioral change typical of aseizure.

The “onset of the electrographic component” of a seizure is defined bythe appearance of a class of signal changes recognized as characteristicof a seizure. This analysis may typically include visual review ofsignal tracings of varying duration, both before and after the perceivedsignal changes, using multiple channels of information and clinicalcorrelates. The precise determination of the onset is subject topersonal interpretation, and may vary based on the skill and attentionlevel of the reviewer, the quality of data, and its display.

An electroencephalogram, or EEG, usually refers to voltage potentialsrecorded from the scalp. The term “EEG” typically encompasses recordingsmade outside the dura mater. The electrocorticogram, or ECoG, typicallyrefers to voltage potentials recorded intracranially, e.g., directlyfrom the cortex. It should be noted that the methods and devicesdescribed herein may be applied to any signal representing electricalactivity sensed from a patient's brain, including EEG and ECoG signals.For simplicity, the term “EEG” has been used throughout this disclosure,and is intended to encompass EEG and ECoG types of signals, as well asany other signals representing electrical activity sensed from apatient's brain.

The period of time during which a seizure is occurring is called theictal period. Those skilled in the art will appreciate that the termictal can be applied to phenomena other than seizures. Periods of timewhen a patient is not in a state of seizure, or in transition into orout of the seizure state, are known as interictal periods.

The term “false positive” refers to the case of a system mistakenlydetecting a non-seizure signal and classifying it as a seizure. The term“false negative” describes the case in which a true seizure goesundetected by a system. Systems that have a low rate of false positivedetections are called specific, while those with a low rate of falsenegative detections are called sensitive.

The term “epileptiform discharge” is used herein to refer to a class ofsharply contoured waveforms, usually of relatively high signal energy,having a relatively brief duration (e.g., rarely exceeding about 200msec). These epileptiform discharge signals (or “spikes”) can formcomplexes with slow waves, and can occur in singlets or in multiplets.

BRIEF SUMMARY OF THE INVENTION

In certain embodiments of the invention, a method of detecting aprecursor to a neurological event includes sampling anelectroencephalograph (EEG) signal to obtain a stream of data values,counting changes in data value amplitude between at least a firstamplitude range and a second amplitude range, and detecting a precursorto a neurological event when the change count exceeds a predeterminedthreshold. The amplitude ranges may be chosen or adapted to identify thepresence of epileptiform discharges, according to certain embodiments ofthe invention.

In an exemplary embodiment of the invention, a computer-readable mediumfor detecting a precursor to a neurological event may includeinstructions for causing a programmable processor to sample anelectroencephalograph (EEG) signal to obtain a stream of data values,count changes in data value amplitude between at least a first amplituderange and a second amplitude range, and detect a precursor to aneurological event when the change count exceeds a predeterminedthreshold. The amplitude ranges may be chosen or adapted to identify thepresence of epileptiform discharges, according to certain embodiments ofthe invention.

Further embodiments of the invention include a system for detecting aprecursor to a neurological event, the system including instructions forcausing a programmable processor to sample an electroencephalograph(EEG) signal to obtain a stream of data values, count changes in datavalue amplitude between at least a first amplitude range and a secondamplitude range, and detect a precursor to a neurological event when thechange count exceeds a predetermined threshold. A system according tocertain embodiments may further include the ability to deliver therapyto a patient when a precursor to a neurological event has been detected.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will hereinafter be described in conjunction withthe following drawing figures, wherein like numerals denote likeelements:

FIG. 1 shows an implantable system for treating a nervous systemdisorder according to an embodiment of the invention;

FIG. 2 is a schematic block diagram of an implantable medical device fortreatment of a nervous system disorder in accordance with embodiments ofthe invention;

FIG. 3 is an exemplary EEG waveform, showing the onset of neurologicalevents corresponding to epileptic seizures;

FIG. 4 shows a simulated EEG waveform, designating portions of aneurological event;

FIG. 5 shows an example of an EEG waveform and a plot of an exemplaryevent monitoring parameter for detecting neurological events inaccordance with various embodiments of the invention;

FIG. 6 is a plot of an exemplary event monitoring parameter associatedwith a seizure detection algorithm;

FIG. 7 is a block diagram showing a method of detecting a neurologicalevent according to an embodiment of the invention;

FIG. 8 is a block diagram of a method of detecting a neurological eventaccording to an embodiment of the invention;

FIG. 9 is a block diagram showing a method of determining a parameterused in the method of FIG. 8;

FIG. 10 is a block diagram showing a method of detecting a precursor toa neurological event according to an embodiment of the invention;

FIG. 11 is a timeline illustrating the determination of parameters usedin the method of FIG. 10;

FIG. 12 is a series of plots corresponding to parameters determined bythe method of FIG. 10;

FIG. 13 is a timeline showing a plot of an event monitoring parameterbefore, during, and after a neurological event. FIG. 13 also includes aseries of time plots showing logical output states associated with amethod of detecting a precursor to a neurological event according to anembodiment of the invention;

FIG. 14 is a block diagram showing a method of detecting a neurologicalevent using EEG and/or cardiovascular signals according to an embodimentof the invention;

FIGS. 15 through 17 are a series of time plots illustrating a method ofidentifying signal saturation according to an embodiment of theinvention;

FIG. 18 is a flow chart showing a method of ensuring signal quality in asignal processing system according to an embodiment of the invention;

FIG. 19 is a timeline plot illustrating the potential effects of therapydelivery on the ability to detect neurological events;

FIG. 20 is a block diagram showing a method of detecting a neurologicalevent following therapy delivery according to an embodiment of theinvention; and

FIGS. 21( a) and 21(b) show time plots of EEG signal data correspondingto simulated post-stimulation neurological events, along with apost-stimulation detection counter according to the method of FIG. 20.

DETAILED DESCRIPTION OF THE INVENTION

The following discussion is presented to enable a person skilled in theart to make and use the invention. Various modifications to theillustrated embodiments will be readily apparent to those skilled in theart, and the generic principles herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present invention as defined by the appended claims. Thus, thepresent invention is not intended to be limited to the embodimentsshown, but is to be accorded the widest scope consistent with theprinciples and features disclosed herein. The following detaileddescription is to be read with reference to the figures, in which likeelements in different figures have like reference numerals. The figures,which are not necessarily to scale, depict selected embodiments and arenot intended to limit the scope of the invention. Skilled artisans willrecognize the examples provided herein have many useful alternativeswhich fall within the scope of the invention as claimed.

FIG. 1 shows an embodiment of an implanted system 10 for treatment of anervous system disorder in accordance with an embodiment of theinvention. System 10 includes IMD 20, lead(s) 19, and electrode(s) 30.Although the implanted system 10 is discussed herein in the context ofmonitoring and recording brain activity and/or providing brainstimulation, it will be appreciated that the implanted system 10 mayalso be used to monitor and record physiological signals from, orprovide treatment therapies to, other locations of the body. The IMD 20could, for example, be a neurostimulator device, a pacing device, adefibrillation device, an implantable loop recorder, a hemodynamicmonitor that does not provide a pacing therapy, or any other implantablesignal recording device known in the art or developed in the future. InFIG. 1, the IMD 20 is electrically coupled to the brain B of patient 12through electrodes 30 and lead conductor(s) of at least one lead 19 in amanner known in the art. The electrodes 30 may also serve as therapydelivery elements to treat nervous system disorders. The IMD 20 maycontinuously or intermittently communicate with an external programmer23 (e.g., patient or physician programmer) via telemetry using, forexample, antenna 24 to relay radio-frequency signals 22, 26 between IMD20 and programmer 23. In this embodiment, each of the features andfunctionalities discussed herein are provided by the IMD 20.

Those skilled in the art will appreciate that some medical devicesystems may take any number of forms from being fully implanted to beingmostly external and can provide treatment therapy to any number oflocations in the body, as disclosed in U.S. Pat. No. 6,341,236 (Osorio,et al.), incorporated herein by reference. For example, the medicaldevice systems described herein may be utilized to provide treatmenttherapy including, for example, electrical stimulation, magneticstimulation, and/or drug infusion. Moreover, it will be appreciated thatthe medical device systems may be utilized to analyze and treat anynumber of nervous system disorders. In the event that closed-loopfeedback control is provided, the medical device system can beconfigured to receive any number of neurological signals that carryinformation about a symptom or a condition or a nervous system disorder.Such signals may be provided using one or more monitoring elements suchas monitoring electrodes or sensors. For example, U.S. Pat. No.6,227,203 provides examples of various types of sensors that may be usedto detect a symptom or a condition or a nervous system disorder andresponsively generate a neurological signal and is hereby incorporatedby reference in relevant part.

FIG. 2 is a schematic block diagram of an IMD 20. The IMD 20 isimplanted in conjunction with a set of electrodes 30. The IMD 20communicates with an external device, such as programmer 23 (FIG. 1),through a telemetry transceiver 1127, an antenna 1125, and a telemetrylink 1123. The external device may collect data from the IMD 20 byplacing antenna 24 on the patient's body 12 over the IMD 20 to therebycommunicate with antenna 1125 of the IMD 20.

IMD 20 may contain an operating system that may employ a microcomputeror a digital state machine for sensing and analyzing physiologicalsignals in accordance with a programmed operating mode. The IMD 20 mayalso contain sense amplifiers for detecting signals, and output circuitsfor delivering electrical stimulation therapy, for example, to certainparts of the brain B. The operating system may include a storage devicefor storing sensed physiological signals, including those associatedwith neurological activity. The storage device may also be used forstoring operating parameters and other operating history data.

Each electrode of the set of electrodes 30 may be adapted to eitherreceive a physiological signal, such as a neurological signal, or tostimulate surrounding tissue, or to perform both functions. Stimulationof any of the electrodes contained in the electrode set 1101 isgenerated by a stimulation IC 1105, as instructed by a microprocessor1119. When stimulation is generated through an electrode, the electrodemay be blanked by a blanking circuit 1107 so that a physiological signalis not received by channel electronics (e.g., amplifier 1111). U.S.Patent Application Publication 2004/0133248 to Frei et al.(“Channel-Selective Blanking for a Medical Device System”), incorporatedby reference herein, discloses a method of blanking signal channelsduring the delivery of therapy. When microprocessor 1119 determines thata channel is able to receive a physiological signal, an analog todigital converter (ADC) 1113 samples the physiological signal at adesired rate (e.g., 250 times per second). Digital logic circuitry,indicated in FIG. 2 by digital logic 1150 and 1160, may be employed toreceive the digitized physiological signal from ADC 113. The digitizedphysiological signal may be stored in a waveform memory 1115 so that theneurological data may be retrieved from the IMD 20 when instructed, ormay be processed by microprocessor 1119 to generate any requiredstimulation signal. In some embodiments, digital logic 1150, 1160 mayemploy a data compression step, such as applying the new turning point(NTP) algorithm or other suitable algorithms or filters, to therebyreduce memory constraints that may be imposed on an IMD due to issues ofsize, power consumption, and cost, for example.

FIG. 3 shows an example of an EEG waveform 40. Epileptic seizures 42, 44may manifest as changes in EEG signal amplitude energy, and/or frequencyfrom an underlying EEG rhythm, as shown in FIG. 3. Also shown areepileptiform discharge spikes 41, which may occur prior to theoccurrence of seizures 42, 44. In certain cases, neurological events,such as seizures 42 and 44, may be thought of as belonging to a singlegroup or cluster of events, for example. Associating a group of eventsas belonging to a single cluster may, for example, be useful in makingdecisions regarding therapy delivery.

FIG. 4 shows a simulated EEG waveform 1901, designating portions of anexemplary neurological event. A time event 1903 corresponds to aninvestigator time of electrographic onset (ITEO), in which a clinicianmay observe a significant amount of electrographic activity on an EEGwaveform 1901 that may mark the beginning of a neurological event suchas a seizure. (However, a neurological event may not necessarily followtime event 1903 in some cases.) A time event 1905 corresponds to analgorithm detection time (ADT), in which a detection algorithm detectsan occurrence of a neurological event based on processing of an EEGwaveform 1901.

A time event 1907 corresponds to a clinical behavior onset time (CBOT),in which a patient typically manifests the symptoms of a neurologicalevent (such as demonstrating the physical characteristics of a seizure).However, in some cases, a patient may not manifest symptoms even thoughan ITEO occurs. Typically, if monitoring elements (such as electrodes)are appropriately positioned, the CBOT 1907 will occur after the ITEO1903. However, depending on the placement of the electrodes relative tothe location of the neurological event, the CBOT 1907 may occur beforethe ITEO 1903 due to potential delays of neurological signalspropagating through various portions of a patient's brain. A time event1909 corresponds to an investigator seizure electrographic terminationtime (ISETT), in which the electrographic activity decreases to a levellow enough to indicate termination of seizure activity. A time event1911 is also provided in FIG. 4 to indicate clinical seizure duration,which may be defined as the time interval from CBOT 1907 to ISETT 1909.

Overview of IMD System

FIG. 5 shows a time plot that generally illustrates the operation of anIMD system in response to an EEG signal in accordance with certainembodiments of the invention. A single channel EEG signal 50 is shown inthe top plot spanning a period of time that includes pre-seizureactivity, seizure onset, therapy delivery, and post-therapy monitoringof EEG signal 50. The bottom plot is an exemplary event monitoringparameter 60 that may be derived from one or more channels of EEGsignals 50. The event monitoring parameter may also be referred to as aseizure monitoring parameter. FIG. 5 shows event monitoring parameter 60starting from a relatively stable or normal value 62, corresponding tonormal EEG signal activity or signal energies (e.g., during interictalperiods). As shown, parameter 60 may increase or decrease due to changesin signal energy, and may cross one or more predefined threshold values64, 66 to indicate the onset (or potential onset) of an epilepticseizure. Parameter 60 is shown crossing threshold 64 at point 65 toindicate the onset of an epileptic seizure 54, in this case identifiedby an increase in parameter 60 above a seizure onset threshold 64. Insome embodiments, a seizure detection algorithm may also require theparameter 60 to exceed the threshold 64 for a specified duration (notshown) in order for the IMD to “detect” the seizure.

Similarly, parameter 60 is also shown dropping below threshold 66 atpoint 67 in FIG. 5 to indicate the possible onset of a seizure accordingto certain optional embodiments of the invention. A specified durationparameter may also be required to be met in order to detect a seizurebased on this type of threshold criterion. As shown, a low-levelthreshold such as threshold 66 may be used to indicate a low level ofEEG signal energy, as shown at 52, which may be used as an earlypredictor or precursor of an epileptic seizure in some embodiments ofthe invention.

The EEG signal 50 in FIG. 5 also shows epileptiform discharge spikes 53,which may also serve as an early predictor or precursor of an epilepticseizure. Certain embodiments of the invention include a method (notshown) for analyzing the occurrence of such spikes 53 and using them to“detect” (i.e., identify a precursor to) a possible seizure.

The methods of detecting seizures and seizure precursors describedherein may be affected by the quality of the signals employed by thevarious methods. For example, periods of signal saturation or clipping,as indicated in EEG signal 50 at points 55, may provide falseinformation to a seizure detection algorithm. Systems and methods formonitoring and accounting for signal quality are disclosed in U.S.Patent Application Publications 2004/0138580 and 2004/0138581 to Frei etal. (both entitled “Signal Quality Monitoring and Control for a MedicalDevice System”), both of which are hereby incorporated by reference intheir respective entireties.

FIG. 5 also illustrates the delivery of therapy 56 from an IMD system inresponse to a detected seizure. The IMD system may provide therapy inthe form of electrical stimulation to portions of the nervous system, orin the form of drug delivery, or in other forms of therapy suitable forthe treatment of an epileptic seizure. FIG. 5 further illustrates theresumption of EEG signal monitoring following the delivery of therapy 56to a patient, as shown in the EEG signal at 58. After successful therapydelivery by the IMD system, parameter 60 may drop below a seizuretermination threshold 68 as shown at point 69, for a predeterminedperiod of time in certain embodiments (e.g., a predefined duration).

An additional or optional aspect of an IMD in accordance with variousembodiments of the invention is also indicated by post-stimulationinterval 70 in FIG. 5. For example, at the termination of therapy 56,the IMD may not immediately have data available from which to derive orcalculate parameter 60 (or data may be “old” data received prior tostimulation therapy, for example). In some embodiments, this may be atleast temporarily addressed by an alternate means of determiningparameter 60 (or a substitute parameter) after the delivery of therapy56, which may quickly assess whether a seizure is still on-going and/ordetermine the need for additional stimulation therapy, for example.

FIG. 6 shows a pair of neurological events detected using a method inaccordance with certain embodiments of the invention. During aneurological event (such as a seizure), EEG activity, as monitored witha seizure detection algorithm, may result in multiple closely-spaceddetections or clusters that a physician/clinician may wish to interpretas being related as part of a single event (e.g., one episode), andwhich, if considered as separate events, may result in an unnecessarytherapy delivery, or possibly an unsafe number of therapy deliveries.This may be particularly true at the beginning or end of a neurologicalevent when oscillations around the detection threshold may result inmultiple closely-spaced detections, which may complicate operations andlogging of events.

A medical device system, e.g., IMD 20, may associate clusters ofclosely-spaced detections using a temporal criterion. For example,detections that are separated in time by less than a programmableinter-detection interval may be classified as being related, and/or maybe deemed to be part of the same cluster or episode. Parameters, such asan inter-detection interval, may be programmable in IMD 20, for example.U.S. Patent Application Publication 2004/0138536 to Frei et al.(“Clustering of Neurological Activity to Determine Length of aNeurological Event”), hereby incorporated by reference in its entirety,discloses such a method of detecting a cluster or clusters ofneurological events.

FIG. 6 shows data 2201 associated with an event monitoring parameter2203, which may be determined by a seizure detection algorithm. A pairof detections is shown, including two periods (duration1, at 2207, andduration2, at 2209) during which event monitoring parameter 2203 exceedsa threshold 2211, as well as a relatively brief intervening period, d1,between 2207 and 2209. Event monitoring parameter 2203 is displayed inFIG. 6 from about 5 seconds before the onset of the first detection toabout 12 seconds after the end of the second detection. A number ofmethods of determining event monitoring parameter 2203 from one or moreEEG signals are described below in later sections.

In certain embodiments, a time constraint may be defined such that, ifevent monitoring parameter 2203 falls below predetermined threshold2211, then subsequently rises above predetermined threshold 2211 (e.g.,a second detection occurs) within the defined time constraint, then thatsubsequent detection is considered to be related to the first detection(e.g., part of the same detection cluster). Thus, the pair of detections2205 includes first duration 2207, the intervening interval, d1, andsecond duration 2209. Analysis of the event monitoring parameter 2203may therefore be performed on clusters or groups of detections, ratherthan solely on individual detected events.

Seizure severity metrics (e.g., measures of the intensity of a detectedseizure) may be based on analysis of the event monitoring parameter 2203over an entire cluster 2205 (rather than on individual detected events),according to certain embodiments of the invention. For example, aseverity metric may be defined as the maximum value of event monitoringparameter 2203 reached during cluster 2205 in certain embodiments. U.S.Patent Application Publication 2004/0133119 to Osorio et al. (“Scoringof Sensed Neurological Signals for use with a Medical Device System”),hereby incorporated by reference in its entirety, discloses such amethod of scoring the severity of sensed neurological signals.

Referring again to FIG. 2, ADC circuit 1113 receives the filtered,amplified physiological signal from electrodes 30, which, in certainembodiments, may be sampled at appropriate rates, such as about 256 or128 Hz or samples per second. Sampling physiological signals at ratesabove about 128 Hz is usually adequate to avoid “aliasing” because thereis typically little energy above 60 Hz included in the sampled signal.“Aliasing” is a phenomenon of the digitization process that may becaused by sampling at too low a sample rate for a given signal,resulting in reproduced signals with spurious or erroneous frequencycontent. Aliasing is typically avoided by performing analog low-passfiltering prior to digitization to limit the frequency content, thensampling at a rate greater than about twice the frequency of the highestfrequency content in the filtered signal. For example, the upperfrequency corner of the analog filter should be no more than half of thesample rate (by Nyquist's Law), and is usually lower than that.

Data signals stored by the IMD 20 may be transmitted between an IMD RFtelemetry antenna 1125 (FIG. 2) and an external RF telemetry antenna 24associated with the external programmer 23 (FIG. 1). In an uplinktelemetry transmission 22, the external RF telemetry antenna 24 operatesas a telemetry receiver antenna, and the IMD RF telemetry antenna 1125operates as a telemetry transmitter antenna. Conversely, in a downlinktelemetry transmission 26, the external RF telemetry antenna 24 operatesas a telemetry transmitter antenna, and the IMD RF telemetry antenna1125 operates as a telemetry receiver antenna. Both RF telemetryantennas 24 and 1125 are coupled to a transceiver including atransmitter and a receiver. This is as described in commonly-assignedU.S. Pat. No. 4,556,063, herein incorporated by reference in relevantpart.

Implantable Seizure Detection Algorithm

As noted above with respect to FIG. 5, an event monitoring parameter 60may be derived from one or more EEG signals to form the basis of aseizure detection algorithm. Several event monitoring parameters 60 maybe derived and used concurrently in certain embodiments, for example, bycombining several such parameters using logical functions (e.g., AND,OR, MAX, MIN, etc.). In the sections that follow, a ratio method and anevidence counter method are described, either or both of which may beused by an IMD to detect the onset of neurological events such asseizures. Several methods are also described below which may anticipateor predict neurological events, for example, by detecting one or moreprecursors of seizure activity.

I. Seizure Detection—Ratio Method

Adverse neurological events, such as epileptic seizures, are typicallycharacterized by increases in EEG signal energy (including increases insignal amplitude and/or frequency). An increase in EEG signal energy(e.g., within a specified frequency range) may be identified ordetected, for example, relative to a reference or background level ofEEG signal energy. An event monitoring parameter may therefore bedefined as a ratio of a relatively recent, short-term representation ofan EEG signal (e.g., the “foreground” of “FG”) to a relatively long-termrepresentation of an EEG signal (e.g. the “background” or “BG”). Theshort-term and long-term representations may be indicative of EEG signalamplitude, energy, and/or frequency, according to various embodiments ofthe invention.

The foreground may, for example, be determined from analysis of an EEGsignal acquired over a first sample interval. The first sample intervalmay be a relatively recent, relatively brief time window in certainembodiments of the invention. In one particular embodiment, a recenttwo-second time window may be used as the first sample interval forcalculating the foreground. In certain embodiments, a median value ofthe EEG signal magnitude over the two-second window may be used as theforeground. Of course, shorter or longer time windows can be chosen fromwhich to base the determination of the foreground, as would be apparentto one of ordinary skill in the art. Similarly, statistical measuresother than the median (e.g., mean, root-mean-square, weighted averages,etc.) may be used to determine a value for the foreground.

A relatively long-term representation of the EEG signal (e.g., thebackground) may be derived from EEG signal data values accumulated overa second sample interval spanning a relatively long period of time(i.e., longer than the first sample interval). For example, a 20-minuteor 30-minute period may be appropriate for the second sample intervalaccording to some embodiments. In certain embodiments, a median value ofthe EEG signal magnitude over the 20- or 30-minute period may be used asthe background. Of course, longer or shorter periods may also be used.Similarly, statistical measures other than the median may also be usedto determine a value for the background.

As mentioned above, a ratio of foreground and background signal energiesmay be defined and used as a criterion for detecting neurologicalevents, such as epileptic seizures. FIG. 7 illustrates one possibleembodiment of the invention in which a ratio 600 is computed from theabove-described foreground and background signals, FG and BG. In theembodiment shown, the ratio may be determined by dividing the foregroundFG by the background BG at function 604, then optionally squaring theresult as shown by the squaring function, U² 602 to produce ratio 600.In certain embodiments, the foreground and background signals, FG andBG, may each be squared (by a function similar to function 602) prior toforming the ratio 600. As would be apparent to one of ordinary skill inthe art, other similar functions may be used to determine ratio 600. Forexample, the optional squaring function 602 may be omitted and/orreplaced with other functions, such as an absolute value function, or adifference function, or a squared difference function, or combinationsof these and other functions.

In certain embodiments of the invention, determining the value of ratio600 may be performed by a method that estimates the ratio using anexponential approximation technique substantially as described incommonly assigned U.S. patent application Ser. No. 10/976,474. Accordingto this technique, a ratio of a numerator (e.g., the short-termrepresentation) to a denominator (e.g., the long-term representation)may be estimated by raising the number 2 to an exponent value, theexponent value being equal to the difference in the most significant setbit (MSSB) positions of the denominator and numerator, respectively. TheMSSB position may be defined as the numbered bit position of a firstnon-zero bit in a binary number, starting from the most significant bit(MSB) of that number. For example, the exponent value may be obtained bydetermining the difference between the MSSB position of the long-termrepresentation and the MSSB position of the short-term representation.The following example illustrates the use of this technique.

Numerator: 01000011 (equals 67 in decimal notation)

Denominator: 00010001 (equals 17 in decimal notation)

The MSSB of the numerator is 2, since the second bit position holds thefirst non-zero bit, starting from the MSB (left-most bit). The MSSB ofthe denominator is 4, since the fourth bit position holds the firstnon-zero bit, starting from the MSB. Applying the technique, an estimateof the ratio of the numerator to the denominator is obtained by raising2 to an exponent value equal to 4−2 (=2). Thus, the estimate is 2²=4,which is reasonably close to the value of 67/17. Of course, variousrefinements and minor modifications to the technique described may beemployed by one of ordinary skill in the art to determine a ratio valuein accordance with embodiments of the invention, and would be consideredto fall within the scope of the invention as claimed.

The onset of a neurological event (e.g., a seizure) may be detected whena predefined ratio 600 of foreground and background signal levels (or afunction derived therefrom) crosses or exceeds an onset threshold. Incertain embodiments, detection of a seizure may further require that theratio 600 exceed the threshold for a specified period of time (e.g.,duration), according to certain embodiments of the invention. This isshown as detection logic 610 in FIG. 7. Seizure detection logic 610 mayfurther include a seizure termination threshold and optionally a seizuretermination duration parameter which may be used to indicate the end ofa seizure episode, for example, when the ratio 600 falls below thetermination threshold for a period longer than the termination duration.The threshold and duration parameters may be pre-defined and/oruser-selectable, and need not be the same for onset and termination.

FIG. 7 also shows the output of detection logic 610 as including twopossible outputs, RATIO_DETECT 612 and RATIO_PRE_DETECT 614.RATIO_PRE_DETECT 614 may change from a logical value of “False” (e.g., avalue of 0) to a logical value of “True” (e.g., a value of 1) when theratio 600 first exceeds the onset threshold, for example. If the ratio600 exceeds the onset threshold for the onset duration, the RATIO_DETECT612 value may also change from a logical value of “False” (e.g., a valueof 0) to a logical value of “True” (e.g., a value of 1). RATIO_DETECT612 and RATIO_PRE_DETECT 614 may both return to “False” (e.g., a valueof 0) if the ratio 600 falls below a predetermined terminationthreshold. Some embodiments may also require that the ratio 600 remainbelow the termination threshold for a predetermined duration beforeassigning a logical value of “False” to the RATIO_DETECT 612 andRATIO_PRE_DETECT 614.

In embodiments using a duration parameter, either for the onsetthreshold or the termination threshold, duration may be defined in anumber of ways. For example, to satisfy the duration parameter, themethod may require that a specified number of consecutive ratio 600values exceed the threshold value before the duration is satisfied.Alternately, the duration parameter may be defined to require thatconsecutive ratio values meet the respective threshold criteria for aspecified period of time. In other embodiments, the duration parametermay be defined such that duration is satisfied, for example, by havingat least a certain number of ratio values within a predefined window ofratio values that exceed the respective threshold values (e.g., apredetermined percentage of values of the ratio must exceed thethreshold for over the given duration parameter). For example, aduration criterion may require that seven out of a rolling window of tenratio values exceed the respective threshold value in order to satisfythe duration criterion. Other possibilities exist for devising aduration criterion, as would be apparent to one of ordinary skill in theart with the benefit of these teachings.

The use of a ratio parameter 600 as a detection criterion may typicallydetect seizures a few seconds after the electrographic onset. It ishypothesized that therapy effectiveness may diminish the longer therapyis delayed from onset. Therefore, to minimize the delay betweendetection of a seizure and delivery of therapy (e.g., electricalstimulation), the output stimulus circuits in an IMD may be adapted tobegin charging prior to seizure detection. For example, the outputstimulus circuits may receive instructions to begin charging whenRATIO_PRE_DETECT 614 becomes True (e.g., a logical value of 1) inembodiments where this marks the beginning of a duration criteria. Thus,the output stimulus circuits may have time to become at least partiallycharged prior to satisfying a seizure onset duration parameter,according to some embodiments of the invention. This may, for example,allow enough time for the stimulus circuits to become fully charged andready to deliver stimulation therapy immediately after duration issatisfied and/or RATIO_DETECT 612 becomes “True.”

The ratio parameter 600 may also be used to determine whether a group ofdetected neurological events are related, for example, as part of asingle seizure cluster or episode. For example, a given neurologicalevent may be considered to be part of the same seizure cluster orepisode as the immediately preceding neurological event if the amount oftime that elapses from the end of the immediately preceding neurologicalevent to the given neurological event is less than a predefined clustertimeout interval.

II. Seizure Detection—Evidence Count Method

In certain embodiments of the invention, an alternate method ofdetecting neurological events such as seizures may be employed, eitheralone or in combination with other methods such as the ratio methoddescribed above. Thus, an event monitoring parameter may be definedusing an “evidence count” technique, as described below.

As shown in FIG. 8, an input stream of EEG sampled data values (e.g.,obtained by sampling an EEG signal at a sample rate) are applied to asignal transform function, U(x) 800, which generates a transformedsignal comprising a stream of data magnitudes values, U_(n). Examples ofsignal transform functions 800 may include, but are not limited to, anabsolute value function, a difference signal (e.g., the magnitude of thedifference between successive input data signals), the square of theinput magnitude, the square of the difference signal, and combinationsof these and other signal transform functions. Suitable signal transformfunctions may, for example, produce positive values derived from thestream of sampled data values. Each transformed data magnitude value,U_(n), is then compared to a magnitude threshold at comparator 802.Comparator 802 produces a stream of comparator output values thatindicate whether or not a given U_(n) value exceeds the magnitudethreshold value. The comparator 802 may produce a stream of comparatoroutput values comprising a stream of binary values (e.g., 0's and 1's)to indicate the results of the evidence count threshold comparisons, ETC804, which may then be input to a memory device 806 (e.g., a shiftregister, data stack, or FIFO buffer) for use in determining an eventmonitoring parameter.

An event monitoring parameter, EVCNT_(n), may be calculated based on arolling sum of the comparator output values (e.g., a rolling sum of thebinary values from ETC 804) in a window/buffer of size N within memorydevice 806. The event monitoring parameter, EVCNT_(n), may then beapplied to seizure detection logic 808 in certain embodiments of theinvention to detect a neurological event such as a seizure. The seizuredetection logic 808 may, for example, incorporate the use of an onsetthreshold such that a neurological event is identified when the eventmonitoring parameter exceeds the onset threshold. Other aspects ofseizure detection logic 808, such as the use of a termination threshold,or the use of a duration parameter for either or both thresholds, mayalso be used and would be comparable to that described above withrespect to the ratio method.

The magnitude threshold used by the comparator may have a pre-definedvalue according to certain embodiments, or may be derived from along-term representation of the EEG signal. A long-term representationof the EEG signal may be determined from the data magnitude values,U_(n), by computing a long-term running average (“LTA”) or other similarmeasures, including a low-pass statistic or an order statistic, forexample without limitation. In certain embodiments, the magnitudethreshold may be proportional to the long-term representation. Forexample, the long-term representation (e.g., LTA) may be multiplied by aseizure threshold factor to obtain the magnitude threshold value. Withcontinued reference to FIG. 8, the magnitude threshold value that iscompared to the U_(n) data magnitude values at 802 may be obtained bymultiplying the LTA_(n) values by a seizure threshold factor, K, asshown at 810. In some embodiments, the seizure threshold factor could bea predetermined value ranging from about 2 to 64, and may be set to anominal value of 22 in certain preferred embodiments. As shown at userselect 812, the seizure threshold factor K may be optionallyuser-selectable in certain embodiments. A number of methods ofdetermining a long-term representation, such as LTA, may be possible.One such method is described below with reference to FIG. 9.

FIG. 9 shows a method of determining LTA values for use with an evidencecount seizure detection method (e.g., as the long-term representation ofthe EEG signal). In certain embodiments, the data magnitude values,U_(n), may be used to calculate LTA by applying the U_(n) values to abaseline filter 900. Baseline filter 900 may be a discrete integratorthat compares each U_(n) value to a previously determined value of LTA,and increments or decrements the value of LTA by a predetermined amount,depending on whether U_(n) is greater than or less than the prior LTAvalue, respectively. In one particular embodiment, baseline filter 900may also establish a maximum and/or a minimum value that may be obtainedby the long-term representation (e.g., LTA). Such an embodiment may beexpressed as follows:

If U _(n) >[LTA _(n-1) ], LTA _(n)=min(LTA _(n-1)+Δ_(incr) , LTA_(max)), and

If U _(n) ≦[LTA _(n-1) ], LTA _(n)=max(LTA _(n-1)−Δ_(decr) , LTA_(min)).

When both increment and decrement amounts, Δ_(incr) and Δ_(decr,) areused, they need not be the same, although they may have the same valuein certain embodiments.

A predetermined initial value of the long-term representation (e.g.,LTA₀) may be provided, or may be selectable, to serve as an initialestimate of the long-term representation in some embodiments. In certainembodiments, the U_(n) values applied to the baseline filter todetermine the long-term representation may be selected by downsamplingthe U_(n) values by a downsampling factor, D, as shown at 902, beforebeing applied to baseline filter 900.

FIG. 9 also shows an alternate embodiment which may be used to disablethe calculation of LTA_(n) values, for example, during periods where aseizure has been detected and may still be in progress. (Continuing tocalculate LTA_(n) values during periods of seizure activity may causethe LTA_(n) values to rise inappropriately, and may affect the abilityof an IMD to detect subsequent seizure activity, for example.) FIG. 9shows a logical “OR-gate” 910 supplying an input to baseline filter 900that effectively acts as an ON/OFF switch for the baseline filter 900.In certain embodiments, a user selection, BF₀ (at 904), may be set to alogical value of “1” to ensure that the baseline filter 900 continues todetermine to LTA_(n) values regardless of the state of seizuredetection. If instead, user selection BF₀ is set to 0, then the outputof the OR-gate 910 (and hence, whether the baseline filter 900calculates LTA_(n) values) is determined by the status of seizuredetection (e.g., by the logical outputs RATIO_DETECT and/orSEIZURE_DETECT), as shown at 906. A logical “NOT” function, 908, may beapplied so that the determination of LTA_(n) values by baseline filter900 occurs only when a seizure detection (or detection cluster) is notin progress.

III. Seizure Precursor Detection—Level Crossing Method

In some embodiments of the invention, a method of identifying a“precursor” to a neurological event such as an epileptic seizure may usea “level crossing” technique that compares incoming EEG signals to oneor more level thresholds (e.g., an upper and a lower level threshold)that define a number of amplitude ranges. The technique may keep trackof crossings between amplitude ranges (e.g., transitions in EEG signalamplitude from one amplitude range to another). The number and/orfrequency of such level crossings may be used to identify the occurrenceof a precursor to seizure activity, such as the presence of epileptiformdischarge spikes, for example, which may occur prior to the onset of anepileptic seizure. Epileptiform discharge activity may typicallymanifest as brief, sudden increases in the amplitude of EEG signals(e.g., spikes), and may have either positive or negative amplitudes.

The identification of precursors to seizures, rather than the seizuresthemselves, may allow more time for a device or system to prepare for aseizure (e.g., to allow time to charge stimulation circuitry needed fortherapy delivery), or may allow for the delivery of preliminary therapythat may be able to prevent or lessen the severity of a subsequentseizure, for example. It has been shown that using such a level crossingtechnique may anticipate the occurrence of a seizure many seconds priorto the electrographic onset, and in some cases, a few minutes prior.

In certain embodiments, a method of detecting a precursor to aneurological event may include sampling an EEG signal to obtain a streamof data values, and applying the data values to a level transform (orclipper transform), which may include one or more level thresholds(e.g., an upper level and a lower level threshold) from which a numberof amplitude ranges or zones can be defined. The level transform may beadapted to produce a stream of output values, each output valueidentifying the amplitude range corresponding to a given data value. Insome embodiments, amplitude ranges may include the following: 1) signalsbelow the lower level (e.g., below a pre-defined negative amplitude), 2)signals above the upper level (e.g., above a pre-defined positiveamplitude), and 3) signals at or between the lower and upper levels. Insome embodiments, a level transform may use a single level threshold todefine two amplitude ranges. In such an embodiment, for example, anabsolute value function may be incorporated so that data values areconverted to positive values before being applied to such a leveltransform.

The output values produced by the level transform may next be applied toa change detector, which produces a stream of change signal values. Forexample, the change detector may produce a change signal value having afirst value if a given output value (e.g., the “current” output value)is different from an immediately preceding output value. The changedetector may produce a change signal value having a second value if acurrent output value is the same as an immediately preceding outputvalue. A count of the first values would therefore provide an indicationof the frequency of level crossings, and thus, may provide a method ofdetecting a precursor to a seizure event. The count of first values maybe taken over a predefined window, which may be defined in terms of timeor as a certain number of output values, for example. In someembodiments, the count of first values may be compared to a precursorthreshold to detect a precursor to a seizure event, detection beingbased on the count exceeding a predefined precursor threshold, forexample.

In certain embodiments, the number of level crossings (e.g., transitionsof signal values from one amplitude range to another) that occur over aspecified period of time may be used to define a “crossing count” value.A “rolling sum” of crossing count values may be used to define a“crossing trend” value, which can be used to identify a precursor to aseizure. For example, when the crossing trend value exceeds apredetermined threshold value (perhaps for a predetermined durationperiod), the identification of a seizure precursor may be said to occur.

In the example that follows, an embodiment of the invention is describedwhich illustrates the use of a level crossing technique to identify aprecursor to neurological events such as epileptic seizures. The exampleis meant to be illustrative in nature, as modifications of the techniquedescribed may be devised by one of ordinary skill in the art with thebenefit of these teachings without departing from the scope of theinvention as claimed.

FIG. 10 is a block diagram that illustrates a level crossing techniquein accordance with certain embodiments of the invention. In theembodiment illustrated, a level transform 200 produces an output havingthree possible values based on the input signals. The inputs may includea signal representative of long-term EEG signal levels, such as abackground level signal 202 (e.g., the BG signal described above withrespect to the ratio technique), and an EEG input signal 204. Thelong-term EEG signal level (e.g., background level signal 202) may, forexample, be used to determine upper and lower level thresholds bydetermining multiples of the background level signal 202. Thus, theupper and lower thresholds may vary over time with changes in thebackground level, and the corresponding amplitude ranges determined bythe upper and lower thresholds may be considered adaptive amplituderanges, according to certain embodiments. This is shown in FIG. 10, asscale factors or multipliers 206 and 208, which are used to producelower and upper level thresholds 210, 212, respectively, equal tomultiples of the background level signal 202, for example. Of course,alternate means of determining values to use for the level thresholds210, 212 in the level transform 200 may be devised, such as usingvarious mathematical formulae and/or logic functions to derive thevalues, or simply using fixed values, or other techniques known in theart.

Having defined the upper and lower level thresholds 210, 212 the leveltransform 200 may be used to transform EEG input signal 204 into astream of output values having three possible values, the output valuesidentifying the amplitude range corresponding to each input data value,as described below:

Level transform output=1 if EEG signal 204>upper level threshold 212;

Level transform output=0 if EEG signal 204 is equal to or between levelthresholds 210 and 212;

and

Level transform output=−1 if EEG signal 204<lower level threshold 210.

Of course, one of ordinary skill in the art would recognize thatdifferent numbers of level thresholds and/or amplitude ranges could beused without departing from the scope of the invention as claimed. Forexample, a single level threshold could be used to define two amplituderanges according to certain embodiments of the invention. The valueschosen for the level threshold(s), and hence the amplitude ranges, mayalso be varied (e.g., for a particular patient) so that they are adaptedto identify epileptiform discharge activity and/or to differentiateepileptiform discharge spikes from normal EEG activity. Such variationswould also be deemed to fall within the scope of the claimed invention.

Next, a level crossing (or level transition) may be defined as occurringwhen the level transform output changes value. This function isillustrated as change detector 214 in FIG. 10. A change detector 214 isadapted to produce a stream of change signal values (ZX) wherein a givenchange signal value, ZX_(n), has a first value (e.g., 1) whenever thecurrent level transform output value is different from the immediatelypreceding level transform output value, and has a second value (e.g., 0)whenever the level transform output value is the same as the immediatelypreceding value. In other words,

ZX _(n)=1, if (Level transform)_(n)≠(Level transform)_(n-1), and

ZX _(n)=0, if (Level transform)_(n)=(Level transform)_(n-1).

ZX is indicated at 216 in FIG. 10. Thus, a ZX value of 1 marks theoccurrence of a transition in amplitude from one amplitude range toanother. In certain embodiments, it may be desirable to define anadditional value to account for large changes in amplitude. For example,in an embodiment where there are 3 amplitude ranges, a change signalvalue, ZX_(n), might be assigned a value of 2 if the signal amplitudechanges from the highest to the lowest amplitude range, or from thelowest to highest.

Next, a level crossing parameter, ZX_CNT 220, may be defined as beingthe number of level crossings or transitions over a specified period oftime, or as the sum of the change signal values, ZX, over a window orblock of N change signal values, according to some embodiments. Forexample:

ZX_CNT=ΣZX_(n) from n=1 to n=N.

In some embodiments of the invention, the windows or blocks of N samplesused to derive the ZX_CNT parameter may be chosen to be non-overlappingsuch that each subsequent determination of ZX_CNT is based on a uniqueblock of ZX data, as shown by block sum 218 and ZX_CNT 220 in FIG. 10.

In certain embodiments, a rolling sum value of the ZX_CNT values from acertain number of windows or blocks of N samples may next be calculatedto determine a level crossing precursor trend count, ZX_TREND_CNT 250,that updates with the determination of each new ZX_CNT value. This isillustrated in FIG. 11, which shows level crossings, ZX, plotted as afunction of time. A block 300 of N samples is shown in which N=10 (e.g.,10 samples per block), and ZX_CNT=5 (e.g., 5 instances of ZX=1 and 5instances of ZX=0 for a sum of 5). In the example shown, a rolling trendbuffer 320 has M blocks of N samples each, where M=60 and N=10. TheZX_TREND_CNT value at a given point in time is the sum of the 60 ZX_CNTvalues in the trend buffer 320. As each new block of N samples isobtained and a new ZX_CNT value determined, the oldest block 300 isdropped from the trend buffer 320, and the newest block 330 is added, sothat a new value of the precursor trend count, ZX_TREND_CNT, may bedetermined.

Referring again to FIG. 10, ZX_CNT 220 values are shown as inputs to atrend buffer 230, from which a rolling sum 240 is computed,substantially as described above with respect to FIG. 11, to obtainZX_TREND_CNT 250. The ZX_TREND_CNT 250 value may next be applied as amonitoring parameter for identifying a precursor to a neurological eventsuch as a seizure, for example, by comparing the ZX_TREND_CNT 250 valueto a predetermined threshold value, which may be a part of detectionlogic 270 in FIG. 10. A neurological event precursor may be identifiedby the occurrence of the ZX_TREND_CNT 250 value exceeding thepredetermined threshold value. In certain embodiments, the ZX_TREND_CNT250 value must exceed the predetermined threshold value for apredetermined duration before a neurological event precursor isidentified. Similarly, the termination of a seizure precursor episodemay be defined by the occurrence of the ZX_TREND_CNT value decreasingbelow a predetermined termination threshold value, possibly for apredetermined termination duration. A level crossing detection output,ZX_DETECT 260, may also be defined, having a logical value of 1 when thedetection criteria have been met (e.g., threshold and durationsatisfied), and a value of 0 prior to the detection criteria being metand/or after the termination criteria have been met (e.g., terminationthreshold and duration satisfied), according to various embodiments ofthe invention.

FIG. 12 shows an example of the use of the level crossing techniqueapplied to sample EEG signal data that includes seizure activitypreceded by epileptiform activity.

As shown in FIG. 13, the ZX_DETECT parameter is shown going from alogical value of 0 to a logical value of 1 at three points in timecorresponding to epileptiform activity and to subsequent seizure events.Thus, the ZX_DETECT parameter provides the ability to identify aprecursor to (e.g., to anticipate) a neurological event such as aseizure, according to certain embodiments of the invention.

IV. Seizure Precursor Detection—Electrodecremental Method

As noted above, seizure activity may be detected by detecting anincrease in recent, short-term EEG signal energy levels as compared tolonger term (e.g., background) levels, as explained with respect to theratio seizure detection method discussed above. It has been observedthat, in certain cases, a neurological event may also be preceded bydecreases in EEG signal energy levels, followed by increases. A methodin accordance with certain embodiments of the invention may identify aseizure precursor to a neurological event due to such decreases in EEGsignal energy, referred to herein as an electrodecremental detectionmethod. An electrodecremental detection method may provide for anadditional or alternate method of identifying a seizure precursor, whichmay lead to earlier seizure detection and thus, to potentially moreeffective therapy.

The ratio parameter described above with respect to a ratio detectionmethod may be used to detect a decrease in EEG signal level. (Otherevent monitoring parameters that compare relatively recent EEG signallevels to longer-term measures of EEG signal levels may also be used,such as the evidence counter method described above.) FIG. 13 shows atime plot 1000 of a ratio 1002 which drops below a minimum ratiothreshold, R_(min) 1004 prior to increasing as a result of seizureactivity. In certain embodiments, a minimum ratio duration parameter mayalso be defined, which must be satisfied to detect a seizure precursor,when used. These parameters may be programmable and adjustable, and maythereby be tuned to reduce the number of false positives that may occur.In certain embodiments, nominal values for R_(min) 1004 may correspondto a ratio value of less than about 0.5, and preferably less than about0.1. In certain preferred embodiments, an R_(min) 1004 value ofapproximately 0.08 may be used. Similarly, a duration parameterassociated with R_(min) 1004 may be selected to an appropriate valuebased on an amount of time or a specified number of sample intervals,for example.

In certain embodiments, a lock-out period may be employed at startup(e.g., when the algorithm is first employed, or after a device reset, orfollowing a previous detected event) to prevent false detections fromthe electrodecremental method. For example, if the background energy isinitialized to a high value, the ratio parameter may tend to haverelatively low values initially, and a false seizure precursor detectionmay occur based on the ratio being below R_(min) 1004. Thus, a lock-outperiod may be employed at startup, and may be defined as a predeterminedtime interval, such as 10 minutes, during which precursor detectionbased on a ratio parameter falling below R_(min) 1004 may be disabled.The lock-out period may similarly be defined to require at least aminimum amount of EEG signal data to be acquired, for example at startupand/or following certain events, before identifying a neurological eventprecursor based on the electrodecremental method.

A lock-out period for the electrodecremental detection method maysimilarly be employed after seizure detection, and/or following thetermination of a neurological event, which may include the duration of aseizure and/or the duration of a cluster of related seizure events, forexample. [As noted above, U.S. Patent Application Publication2004/0138536 to Frei et al. (“Clustering of Neurological Activity toDetermine Length of a Neurological Event”), which is incorporated byreference herein, discloses a method of detecting a cluster or clustersof neurological events.] This may reduce false detections based upondetection of a post-ictal electrodecremental response, which may occurin some cases due to “post-ictal quieting” following a seizure episodeor cluster. In some embodiments, the lock-out period may extend for acertain predefined period beyond the duration of a seizure cluster, forexample, with a lock-out period extending approximately 2 minutes beyondthe cluster duration in one particularly preferred embodiment. Ofcourse, occurrences other than the end of a cluster timer may also beadapted to trigger the electrodecremental lockout period. Examples ofsuch occurrences include, but are not limited to, the following: theevent monitoring parameter dropping below a detection threshold, the endof a period of therapy delivery, or the event monitoring parameterdropping below a lower threshold level following a detected neurologicalevent, for example.

With continued reference to FIG. 13, note that a single event monitoringparameter (e.g., ratio 1002) is plotted with respect to a minimumthreshold, R_(min) 1004, as well as to a maximum detection threshold,R_(max) 1006. Of course, a different event monitoring parameter, ormultiple event monitoring parameters in combination (e.g., using a MINfunction), could be used in conjunction with the threshold R_(min) 1004for the electrodecremental method than that which is used with thedetection threshold R_(max) 1006 for the ratio method, according tovarious embodiments of the invention; a single parameter is used in FIG.13 to facilitate the explanation.

The timeline 1000 of FIG. 13 shows several events of interest regardingthe use of the electrodecremental detection method, as reflected in thelogic states 1020 shown beneath timeline 1000. At time t₁, for example,the event monitoring parameter drops below threshold R_(min) 1004. Attime t₂, a duration parameter associated with R_(min) 1004 has beensatisfied, which results in the detection state 1022 going from alogical 0 to a logical 1. The cluster state 1024 also goes from 0 to 1at time t₂, and a cluster timer begins. In certain embodiments,stimulation therapy may begin to be delivered at time t₂ as well. Incertain other embodiments, charging of stimulation circuitry maycommence at time t₂ in anticipation of a neurological event. At time t₃,the electrodecremental detection method 1026 is disabled, in thisexample, shortly after detection. Also, since this method is a seizureprecursor detection method, no attempt is made to detect a terminationof the electrodecremental detection. The detection state 1022 returns to0 as a result, however the cluster state 1024 remains 1.

At time t₄, parameter 1002 exceeds the max ratio threshold, R_(max) 1006corresponding to the above-described ratio detection method. At time t₅,a duration parameter associated with R_(max) 1006 is met and thedetection state is again set to 1, and the cluster timer is reset. Attime t₆, parameter 1002 drops below the max ratio threshold, R_(max)1006. In the particular example shown, a termination duration of 0 isused, so the detection state 1022 immediately returns to 0. At times t₇,t₈, and t₉, the same process of detection and termination as thatdescribed for times t₄, t₅, and t₆ occurs. At times t₁₀, and t₁₁, theevent monitoring parameter 1002 drops below R_(min) 1004, and theduration parameter is satisfied, but no electrodecremental precursordetection occurs here, since the electrodecremental detection method hasbeen disabled to prevent a false detection during a period of post-ictalquieting, such as that shown following time t₉ in FIG. 13.

At time t₁₂, the cluster timer times out (since there have been nofurther detections since time t₉), and the cluster state returns to avalue of 0. The cluster time-out interval in this example corresponds tothe period from t₉ to t₁₂. As shown, the electrodecremental detectionmethod remains disabled (or “locked out”) for a period following the endof the cluster corresponding to the time period from t₁₂ to t₁₃. At timet₁₃, the post-cluster lock-out period expires, and theelectrodecremental detection method is again enabled.

As noted above, other event monitoring parameters may be used inconjunction with an electrodecremental method of detecting a seizureprecursor. For example, the evidence count method may be modified toallow detection of a seizure precursor in accordance with theelectrodecremental method. In one possible embodiment, rather thandetermining whether incoming data magnitude values exceed some multipleof the long-term average (LTA), a multiple of the data magnitude valuescould be compared to the LTA to determine whether they are below theLTA. For example, if each incoming data magnitude value is multiplied byscale multiple (e.g., a factor of 10), then compared to a magnitudethreshold (e.g, the LTA), a stream of comparator output values could begenerated whereby a logical 1 could indicate that the value is below themagnitude threshold. The remainder of the evidence count algorithm wouldoperate substantially as described above and would allow for thedetection of a seizure precursor in accordance with theelectrodecremental method.

Other variations and modifications may become apparent to one ofordinary skill in the art with the benefit of these teachings and wouldbe deemed to fall within the scope of the invention as claimed.

V. Seizure Precursor Detection—Neuro-Cardiovascular Signal Analysis

It has been observed that certain types of information, when used inconjunction with EEG signal analysis, may be useful in improving thespecificity with which seizures may be anticipated. For example,analysis of cardiovascular (CV) signals, including electrocardiogram(ECG) and hemodynamic signals (e.g., blood pressure signals), may beperformed in conjunction with EEG signal analysis to predict oranticipate seizures according to certain embodiments of the invention.

A method of predicting a seizure event may involve acquiring EEG and CVsignals, extracting certain “features” from the EEG and CV signals, andusing the extracted features to derive a discriminant measure. Thediscriminant measure may, for example, be a weighted sum of theextracted EEG and CV features. The discriminant measure may then becompared to a predetermined threshold to predict a seizure event.

The features extracted from the EEG and cardiovascular signals mayoptionally be compared to a similarity measure to determine how similarthe extracted features are to those obtained from the same patient (orfrom a representative or similar patient) prior to or during an actualobserved seizure event, according to certain embodiments of theinvention. Likewise, the features extracted from the EEG andcardiovascular signals may also be compared to a dissimilarity measureto determine how dissimilar the extracted features are to those obtainedfrom the same (or a similar) patient prior to or during periods ofnormal or baseline activity, according to certain embodiments of theinvention. The similarity and dissimilarity measures may also be updatedto incorporate new information in some embodiments.

FIG. 14 shows a block diagram of a method of predicting seizure eventsusing both EEG signals and cardiovascular (CV) signals. EEG and CVsignals may be acquired in any manner known in the art. In certainembodiments, a feature extraction process may be applied to bothsignals, which may include a signal transformation or characterizationresulting in an output signal. For example, process step 1110 in FIG. 14may receive one or more acquired EEG signals as an input, and mayextract certain “features” from the EEG signals that describe the EEGsignals in terms of quantitative values, for example. The featureextraction process of step 1110 may include the determination of one ormore of the various event monitoring parameters described above(including any intermediate parameters determined), such as a foregroundsignal (FG), a background signal (BG), a ratio of FG and BG, a long-termaverage (LTA), evidence counts, level crossing counts, and levelcrossing trend counts, for example without limitation. Other features orsets of features may be determined and used as well.

An example of extracting EEG features may include the use of a“zero-crossing” technique. A zero-crossing technique may use the timingof EEG signal polarity changes to derive EEG features. For example, thetime intervals between zero crossings (e.g., between signal polaritychanges) may be determined, and a measure of the time intervals (e.g., astatistical representation) over a given time frame may be computed toproduce one or more of the EEG features. In certain embodiments, zerocrossings in the same direction may be employed as the basis fordetermining the time intervals. For example, the time intervals betweentransitions in signal polarity from negative to positive values (or viceversa) may be used. In some embodiments, the statistical representationof the time intervals may be computed as the mean value and/or standarddeviation of the time intervals over a given time frame, or for a numberof periodic time frames (e.g., successive time frames), for example.

In certain embodiments, the statistical representation of the timeintervals may be computed for a number of different frequency bands, forexample, by applying the EEG signal to one or more passband filtersprior to determining the time intervals and statistical representations.Passband filters corresponding to physiological frequency sub-bands maybe employed, according to some embodiments of the invention. Suchphysiologic frequency sub-bands may encompass a range of frequenciesfrom 0-50 Hz, and may include sub-bands at 1-4 Hz, 4-8 Hz, 8-12 Hz, and12-40 Hz, according to some embodiments of the invention. Of course, theparticular frequency bands and sub-bands chosen may vary from theseand/or may be adapted for particular patients, according to certainembodiments of the invention.

In other embodiments, the extracted features (such as the statisticalinformation regarding the timing of zero crossings, described above)from a number of different EEG signal channels may be compared to eachother to compute a measure of synchronization, for example, usingcross-correlation or other suitable measures. A measure ofsynchronization may also be computed for the features extracted from twoor more frequency sub-bands, according to certain embodiments.

With continued reference to FIG. 14, process step 1112 may receive oneor more cardiovascular (CV) signals as inputs, including ECG andhemodynamic signals. Step 1112 may entail one or more different types offeature extraction from the CV signals, including assessments of changesin heart rate (e.g., heart rate trend information), cardiachyper-excitability (or marginality), and autonomic nervous system (ANS)modulation, for example. Feature extraction in step 1112 typicallyinvolves characterizing physiologic signals, such as ECG signals andblood pressure signals, in terms of parameters that can be measured andanalyzed. Features that may be extracted from an ECG signal may include,but are not limited to, heart rate, as determined by R-R intervals(i.e., the intervals between intrinsic ventricular depolarizations), Q-Tintervals, measures of heart rate variability (non-parametric andparametric), and rates of increase or decrease in heart rate. Otherfeatures that may be extracted from cardiovascular signals may includemeasures of blood pressure (e.g., systolic and diastolic) and flow, forexample. In certain embodiments, a multi-dimensional analysis of heartrate and blood pressure may be used to derive an indicator (orindicators) of autonomic nervous system (ANS) modulation (by usingtechniques such as blind source separation, for example). A techniquefor deriving an index of ANS modulation using blind source separation isprovided in U.S. Published Patent Application 2004/0215263. Severalfeature extraction methods are described below in more detail.

The outputs of steps 1110 and 1112 are the values of the featuresextracted from both the EEG and cardiovascular signals, respectively.The features are then input to a discriminator 1120, which produces adiscriminant measure signal (e.g., an event monitoring parameter), whichmay be applied to seizure anticipation logic 1130. For example, the EEGfeatures from step 1110 may be combined with the cardiovascular featuresfrom step 1112 according to combinational logic in the discriminator1120 to derive a discriminant measure, which may improve the specificityof seizure anticipation logic 1130. Combinational logic may, forexample, comprise weighting the various features extracted according toreliability or importance, and forming a weighted sum of the features toproduce a discriminant measure (or event monitoring parameter) for inputto seizure anticipation logic 1130.

Seizure anticipation logic 1130 may analyze the incoming discriminantmeasure to make a decision regarding prediction of a seizure event. (Adecision to “predict” or “anticipate” a seizure event may be made priorto a seizure actually occurring, and merely indicates that a seizure islikely to occur; an actual seizure event may not necessarily follow aprediction decision. Thus, the terms “prediction” and “anticipation”have been used here rather than “detection” to distinguish from methodswhich detect the actual occurrence of a seizure.) A threshold level andan optional duration parameter may be included as part of seizureanticipation logic 1130. For example, a seizure event may be predictedwhen the discriminant measure exceeds a predetermined threshold for apredetermined duration, according to certain embodiments.

In certain embodiments of the invention, the ability to predict aseizure event in a particular patient may be improved by including a“similarity” measure 1160 as part of the discriminator 1120. Similaritymeasure 1160 may be used to compare the features extracted (from eitheror both of steps 1110 and 1112) to the features corresponding to a“reference seizure” 1140 (e.g., features representative of seizures inthe same patient or a similar patient). A determination may be made ofhow similar the current extracted features are to those of the referenceseizure 1140, which may affect the weighting assigned to variousfeatures and/or the calculation of the discriminant measure. Similarly,the ability to predict a seizure may be improved by using a“dissimilarity” measure 1170 (either alone or in conjunction with thesimilarity measure), which compares the features extracted (from eitheror both of steps 1110 and 1112) to the features corresponding to a“reference baseline” 1150 (e.g., features representative of periods ofnormal or baseline activity from the same patient or a similar patient).A determination may be made of how dissimilar the current features areto the baseline reference 1150, which may likewise affect the weightingassigned to various extracted features and/or the calculation of thediscriminant measure.

In further embodiments, the similarity and dissimilarity measures 1160,1170 may be further enhanced by having the ability to provide updates toeither or both of the seizure reference 1140 and baseline reference 1150values. The updates may comprise new seizure reference or baselinereference information obtained for a particular patient, for example.The seizure reference may be updated by replacing the existing seizurereference information with new information from a recent seizure eventfor a particular patient, in one possible embodiment. In otherembodiments, the seizure reference may be updated by adding orincorporating a recent seizure event to the existing seizure referenceto form a weighted average, for example. Updates to the baselinereference may be made in manner analogous to that just described for theseizure reference.

Feature extraction of cardiovascular signals may be based on changes(e.g., increases) in heart rate in some embodiments. For example, afeature may provide an indication of whether heart rate has increasedabove a certain rate (e.g., tachycardia) in certain embodiments. Inother embodiments, a feature may indicate whether the heart rate hasincreased (or decreased) suddenly, for example, by greater than X beatsper minute within a predefined time period.

Feature extraction of cardiovascular signals may also be based oncardiac hyper-excitability (or marginality) in some embodiments.(Marginality reflects the presence and/or amount of non-coordinatedchronotropic responses.) For example, an extracted feature describingthe marginality of a cardiovascular signal may include statisticalinformation about R-R intervals over predetermined time intervals (e.g.,every six minutes). An extracted feature describing the marginality of acardiovascular signal may also indicate the number of ectopic andmarginal events over a given time interval, for example.

Another method of feature extraction of cardiovascular signals may bebased on autonomic nervous system (ANS) activity or modulation incertain embodiments. A method of determining an indicator (or index) ofANS modulation is disclosed in commonly assigned U.S. patent applicationSer. No. 10/422,069, relevant portions of which are incorporated byreference herein. In certain embodiments, R-R intervals and bloodpressure measurements may be used to derive an index of ANS modulationusing multi-dimensional analysis, for example, using a technique such asblind source separation. If only R-R intervals are available, forexample, classical heart rate variability analysis can be used(parametric or non-parametric).

Signal Quality—Clip Count Algorithm

In the seizure detection methods described above, the input signals wereassumed to be of good quality. However, certain situations or problemsmay arise that cause an input signal to “flat line,” saturate, stick toone rail or the other, bounce between rails, or otherwise deteriorate inquality. For example, a fractured or dislodged lead may cause the inputsignal to “rail” high or low. Since the above described seizuredetection methods rely on the quality of the input signals to possiblyform the basis for episode storage and/or therapy delivery or otherdecision-making processes, it would be desirable to provide a method ofdisabling detection methods in the presence of such problematic inputsignals, and subsequently re-enabling detection methods once suchsignals are no longer present.

In an embodiment of the invention, a method is described for detecting“clipping” of input signals that may affect a seizure detectionalgorithm, such as those described above. In certain embodiments of theinvention, an IMD may be adapted to perform a method which analyzesinput signals to detect clipping of the input signals, and which mayfurther disable or enable processing of a seizure detection algorithm inresponse to such analysis. Although embodiments of the invention will bedescribed below in the context of an implantable seizure detectionalgorithm, one of ordinary skill in the art with the benefit of theseteachings will recognize that the methods and devices described hereinmay be used in other signal sensing and processing applications.

One way of defining whether a signal has been “clipped” is bydetermining when the difference in amplitude between consecutive inputdata points is less than or equal to some predefined parameter, forexample, according to certain embodiments. The predefined parameter, or“clipping tolerance,” Ct, may be defined using the following logic:

If |x _(n) −x _(n-1) |≦C _(t), then data point x_(n) may be said to be“clipped,”

where x_(n) and x_(n-1) are the signal values of consecutive datapoints. The clipping tolerance, C_(t), may be set to a value of zero incertain embodiments, thereby requiring that x_(n) and x_(n-1) be equalto each other to indicate clipping. In other embodiments, it may bedesirable to use other (e.g., non-zero) values for C_(t). A zero valuefor C_(t) may be appropriate, for example, in embodiments where theinput signals comprise digital data, e.g., binary representations ofsignal levels. In such an embodiment, the clipping tolerance iseffectively equal to the resolution of the least significant bit. Inembodiments using non-zero values for clipping tolerance, C_(t) could bedefined in terms of a specified number of bits of signal resolution. Forexample, if C_(t) is set to 2 bits, then a data point with a binarysignal value of “000 001” following a data point with a binary signalvalue of “000 011” would be identified as a clipped data point, since ithas an amplitude that is within 2 bits of signal resolution from theamplitude of the preceding data point.

A method in accordance with an embodiment of the invention may attemptto determine whether a relatively high percentage of recent data pointsare clipped, indicating that there may be a problem with signal quality.In one embodiment, a running measure of clipped signals, referred toherein as the “clip count,” or C_(c), may be obtained by evaluatingsuccessive data points against the clipping tolerance, C_(t), and eitherincrementing or decrementing the clip count based on the result asfollows:

If |x _(n) −x _(n-1) |≦C _(t), then C _(c) =C _(c)+1,

else C _(c) =C _(c)−1.

In certain embodiments, clip count C_(c) may be initialized to a valueof 1, for example. In certain further embodiments, clip count C_(c) maybe based upon an evaluation of a rolling window or buffer of apredetermined number of sample points (or equivalently, a number ofsample points acquired over a defined first period), and may bedetermined as a weighted average, or other appropriate measure of signaldata. In some embodiments, the clip count parameter, C_(c), may bedetermined as a running measure (e.g., an unbounded first period), asdescribed by the above equation, but may be bounded by a maximum value,C_(max), and/or a minimum value, C_(min) (e.g., a ceiling and a floorvalue, respectively). For example,

If C _(c) >C _(max), then C _(c) =C _(max), and

if C _(c)<C_(min), then C _(c) =C _(min).

In order to use the clip count, C_(c), to control the input signalquality for a seizure detection algorithm, a saturation threshold value,T_(d), and a non-saturation threshold, T_(e), may be defined todetermine when to disable seizure detection and/or precursor detectionsignal processing, as well as when to re-enable signal processing,respectively. In certain embodiments, the threshold values may beincorporated into decision-making logic as follows:

If C _(c) ≧T _(d), disable processing of a seizure detection algorithm,

and if C _(c) ≦T _(e), re-enable processing of a seizure detectionalgorithm.

In certain further embodiments of the invention, a duration parametermay also be defined such that C_(c) must exceed threshold T_(d) for apredetermined period of time (e.g., duration D_(d)) before seizuredetection processing is disabled, and C_(c) must drop below thresholdT_(e) for a predetermined period of time (e.g., duration D_(e)) beforeseizure detection processing is re-enabled.

It should be noted that “disabling” signal processing, as describedabove, may comprise suspending data input, or suspending the processingof data by a seizure detection algorithm, or suspending any outputgenerated by a seizure detection algorithm, or some similar actions orcombinations of actions. Similarly, “enabling” signal processing (e.g.,when a signal saturation condition terminates) may typically involvereversing the actions taken to disable signal processing, but mayinclude alternate or additional steps as well.

FIG. 15 shows an example of the above-described clip count algorithmbeing used to evaluate signal quality during analysis of an EEG signal.Raw EEG signal 710 is shown in the upper pane of FIG. 15, signal 710having a flat line or saturated portion, as indicated at 712. The clipcount C_(c) signal 714 is shown in the middle pane of FIG. 15, and adisable function 716 is shown in the lower pane of FIG. 15. In theparticular example shown, a floor value for clip count C_(c) is set atC_(min)=1, a ceiling value for clip count C_(c) is set at C_(max)=4000,a threshold T_(d) for disabling the seizure detection algorithm is setat T_(d)=500, and the threshold T_(e) for re-enabling the seizuredetection algorithm is set at T_(e)=50.

As shown in FIG. 15, as EEG signal 710 saturates at 712, clip countC_(c) signal 714 begins to increase due to the incrementing of C_(c)described above. When clip count C_(c) signal 714 reaches a value of 500(corresponding to threshold T_(d)), the disable function 716 becomestrue, corresponding to a change in logical value from 0 to 1 as shown.When the saturated portion 712 ends, clip count C_(c) signal 714 beginsto decrease linearly due to the decrementing of C_(c) described above.When clip count C_(c) signal 714 drops to a value of 50 (correspondingto threshold T_(e)), the disable function 716 becomes false,corresponding to a change in logical value from 1 to 0 as shown.

FIG. 16 shows the same signals 710, 714, and 716 shown in FIG. 15, butincludes a greatly enlarged view of signal 714 wherein the vertical axisfor the clip count only extends from a value of 0 to 5. This viewillustrates the robustness of the clip count algorithm by revealing thatthe clip count C_(c) signal 714 tends to remain at a relatively lowlevel (e.g., between 1 and 5) when processing valid physiologic signals(e.g., without saturation of the amplifiers and/or analog-to-digitalconverters). Note, for example, that the clip count C_(c) signal 714does not exceed a value of 4 in the entire frame shown with theexception of the portion corresponding to the flat-lined portion 712.Thus, the clip count C_(c) signal 714 does not even approach thethreshold value (T_(d)=500) for disabling a seizure detection algorithmin the example shown in any portion other than flat-lined portion 712.

FIG. 17 again shows signal 710 from FIGS. 15 and 16, but includes asituation where clipping of signal 710 has been imposed by clipping thesignal 710 within a range of values between 1900 and 2200 as shown.Despite what appears to be a significant amount of clipping throughoutthe raw EEG signal, the clip count C_(c) tends to remain relatively lowfor all portions of the signal other than the actual flat-lined portion712, and does not again exceed the threshold T_(d) after returning belowthe threshold T_(e) following the flat line portion 712. This situationmight occur, for example, if the gain is set too high for the amplifieror A/D converter. As shown, the clip count algorithm is able todistinguish between a truly saturated signal (e.g., flat-lined portion712 and) apparent clipping in other portions of the EEG signal 710 whichmay be caused by device settings, for example.

FIG. 18 is a flow chart of a method of ensuring signal quality byidentifying poor signal conditions, due to such problems as clipping andsignal saturation, for example. Step 750 involves determining thedifference in amplitude between consecutive EEG signal data samples. Atstep 752, the magnitude of the difference between consecutive datasample values is compared to a clipping tolerance. If the magnitude ofthe difference between a given data sample and the preceding data sampleis less than the clipping tolerance, then step 754 is applied toincrement the value of the clip counter, C_(c). Optionally, as shown instep 754, a ceiling value, C_(max) may be employed to put a limit on howlarge the clip counter may become. The clip counter value determined atstep 754 is then compared to a disable threshold, T_(d), as shown asstep 756. If the clip counter value exceeds the disable threshold, thensignal processing may be disabled, as shown as step 758. On the otherhand, if the clip counter value does not exceed the disable threshold,then the process returns to step 750 to continue analyzing incoming EEGsample values. If, at step 752, the magnitude of the difference inconsecutive signal values was found not be less than the clippingtolerance, then step 760 may be employed to decrement the value of theclip counter, as shown. In certain embodiments, an optional floor may beemployed such that the clip counter value cannot decrease below apredetermined amount, C_(min). After decrementing at step 760, the valueof the clip counter is compared to an enabling threshold, T_(e), asshown as step 762. If the clip counter value is less than the enablethreshold, then signal processing may be re-enabled, as shown as step764. Otherwise, the process returns to step 750 to continue analyzingEEG signal values.

Post-Stimulation Detection Algorithm (PSDA)

As noted above, a device that uses a seizure detection algorithm inaccordance with various embodiments of the invention may be adapted todeliver therapy in response to a detected seizure event. A device orsystem according to certain embodiments of the invention may includeleads adapted to perform both sensing and stimulation functions. Duringthe delivery of stimulation therapy from such leads, a seizure detectionalgorithm may be at least temporarily disabled, e.g., to protectamplifier circuitry and/or avoid processing meaningless data. This maybe accomplished through the use of hardware blanking, where no data iscollected, or through the use of software blanking, where data may becollected on channels not being used for stimulation, but where the datais not processed by a seizure detection algorithm. Following therapydelivery, a time delay may be imposed during which stabilization isallowed to occur prior to analyzing signals for the continuing presenceof a neurological event. U.S. Patent Application Publication2004/0152958 to Frei et al. (“Timed Delay for Redelivery of TreatmentTherapy for a Medical Device System”), hereby incorporated by referencein its entirety, discloses such a method of using a time delay followingtherapy delivery for a neurological event.

Upon the completion of stimulation therapy delivery, it may be desirableto quickly determine the need for additional stimulation therapy, sincethe effectiveness of such therapy may diminish with time. The seizuredetection algorithm used to detect the seizure event and triggerstimulation therapy in response thereto may not be ideally suited forrapidly determining the need for additional subsequent stimulationtherapy. The foreground signal (FG) described above, for example, maytake several seconds following stimulation therapy to resume providing aratio calculation based on post-stimulation data. Since time delays indelivering therapy are believed to be a factor in determining thesuccess of a therapy, a method is desired that can quickly determinewhether a seizure episode is still in progress following the delivery ofstimulation therapy and/or assess the need for additional stimulationtherapy. Such a method may be used following a stimulation therapy untilenough time has elapsed to allow for a return to the “normal” seizuredetection algorithm, for example.

FIG. 19 shows a timeline drawing that illustrates the above-describedsituation. FIG. 19 shows a timing diagram for an event monitoringparameter 2301 in accordance with certain embodiments of the invention.Parameter 2301 may, for example, be a ratio of foreground to backgroundEEG signal energy as described above with reference to FIG. 7. The eventmonitoring parameter 2301 may further comprise a maximal ratio, forexample, the largest ratio of a set of ratios (e.g., from multiple EEGsignal channels), in which each ratio is determined by a short-termrepresentation of a neurological signal divided by a correspondinglong-term representation.

Signal data 2300 comprises signal segments 2305, 2307, 2309, 2311, and2313. During segment 2305, signal data 2300 is collected, processed, andtracked by the medical device system in order to determine if a seizureis occurring. As a result of the seizure detection at the end ofinterval 2305 (e.g., based on the seizure detection algorithm's analysisof input signal data 2300 during time interval 2335), the medical devicedelivers an electrical stimulation pulse 2315 to a desired set ofelectrodes. Other embodiments of the invention, of course, may use formsof therapeutic treatment other than an electrical stimulation pulse, orin conjunction with an electrical stimulation pulse.

During stimulation pulse 2315, a corresponding channel is blanked byhardware during a hardware blanking interval 2325 so that no signal iscollected or analyzed during this interval of time. A software blankinginterval 2329 is also shown. During software blanking interval 2329, forexample, the medical device system does not process signal data acquiredduring segments 2307 and 2309. In some embodiments, the medical devicesystem may not collect signal data during software blanking interval2329, while in other embodiments, the signal data may be acquired butnot processed. In certain embodiments, software blanking may occur on asubset of all channels, including channels not being stimulated. Also,the set of channels that employ software blanking may be different fromthe set of channels that employ hardware blanking. U.S. PatentApplication Publication 2004/0133248 to Frei et al. (“Channel-SelectiveBlanking for a Medical Device System”), hereby incorporated by referencein its entirety, discloses such a method of blanking certain channelsduring the delivery of therapy from one or more of the channels.

After software blanking interval 2329, the medical device system mayresume analyzing signal data 2300 using a seizure detection algorithmduring recovery interval 2323 and may produce an output corresponding tosegment 2311 in FIG. 19. As noted, a seizure detection algorithm mayutilize a relatively short-term representation of EEG signal energy,such as the approximately two-second foreground window, FG, according tocertain embodiments of the invention. The algorithm recovery interval2323 in such an embodiment would therefore be approximately two seconds.Meaningful data 2337 acquired after the algorithm recovery interval 2323may thereafter be used to determine whether treatment therapy waseffective, or whether the seizure is continuing. However, intervals 2323and/or 2337 may represent periods of time during which additionaltherapy may be warranted, and during which delays in delivering therapymay reduce the effectiveness of such additional therapy.

A method of detecting a seizure event following delivery of stimulationtherapy is described below with reference to FIGS. 19 and 20. FIG. 19illustrates the operation of a post-stimulation seizure detectionalgorithm that may be used in conjunction with a “normal” seizuredetection algorithm, according to an embodiment of the invention. Forexample, stimulation therapy 2315 may be delivered by an IMD upondetection of a seizure (beginning of segment 2307) using a “normal”seizure detection algorithm (e.g., by signal 2301 exceeding threshold2351 for a predetermined duration). The normal seizure detectionalgorithm may rely on both long-term and short-term EEG signalrepresentations, for example. Following stimulation 2315, apost-stimulation detection period 2311, 2313 may occur, during which apost-stimulation detection algorithm may operate, either alone or inconjunction with the normal seizure detection algorithm. The output ofthe post-stimulation detection algorithm may be used, for example, toallow for detection (e.g., re-detection) of seizure activity during thepost-stimulation detection period 2311, 2313 according to certainembodiments. This may be desired to provide a post-stimulation seizuredetection algorithm which can assess the need for additional stimulationtherapy, and trigger such therapy, until at least the short-termcomponent of the normal seizure detection algorithm acquires sufficientpost-stimulation data to allow resumption of the normal seizuredetection algorithm.

Once the short-term component of the normal seizure detection algorithmhas a sufficient amount of post-stimulation data, seizure detection mayresume according to the normal seizure detection algorithm, as shown atperiod 2337 in FIG. 19.

In certain embodiments of the invention, a post-stimulation detectioncounter, C, may be defined using post-stimulation data. A method ofdetermining and using a post-stimulation detection counter, C, to detectseizure activity following delivery of stimulation therapy is shown inFIG. 20. The method shown may be employed upon delivery of stimulationtherapy (or soon thereafter), as indicated at 2440. For example,post-stimulation detection counter, C, may be initialized to an initialvalue, C₀, following delivery of stimulation therapy, as shown at 2442.The initial value, C₀, may be set to a value of zero, or may be set tosome other value (e.g., 200) according to user preference, for example.A stream of post-stimulation EEG signal data values, U_(n), is acquiredas shown at 2444 and compared to a level cutoff at 2446. The signal datavalues, U_(n), may comprise input amplitude data obtained at a samplerate (e.g., 250 samples per second) according to certain embodiments.

If a given U_(n) value is equal to or exceeds the level cutoff 2446 asdetermined at step 2448, the post-stimulation detection counter, C, isincremented by a specified increment amount (e.g., C_(n)=C_(n-1)+1), asshown at step 2450. If instead, a given U_(n) value is below the levelcutoff 2446 as determined at step 2448, the post-stimulation detectioncounter, C, is decremented by a specified decrement amount (e.g.,C_(n)=C_(n-1)−1), as shown at step 2452. More generally,

If U _(n) ≧k*BG, Then C _(n) =C _(n-1)+(increment)_(PS)

Else, C _(n)=C_(n-1)−(decrement)_(PS),

where k*BG represents the value of the level cutoff 2446 (discussed inmore detail below), and where (increment)ps and (decrement)_(PS) are theincrement and decrement amounts, respectively.

In certain embodiments of the invention, the values of (increment)_(PS)and (decrement)_(PS) may be set to integer values, such as 0, 1 or 2. Incertain preferred embodiments, both values may be set to 1.

The value of the post-stimulation detection counter, C, may next becompared to a post-stimulation detection threshold, PS_(th), as shown at2454, for example, after incrementing C. If the value of C equals orexceeds the post-stimulation detection threshold, PS_(th), apost-stimulation seizure event may be considered “detected,” asindicated at 2456. Additional stimulation therapy may be delivered inresponse to a detected post-stimulation seizure event, as shown at 2458.An optional duration parameter, PS_(dur), could also be defined (inwhich case, C would need to equal or exceed PS_(th) for the prescribedduration parameter to cause a detection), but PS_(dur) would typicallybe given a value smaller than the duration value used (if any) duringnormal seizure detection processing.

In a particular exemplary embodiment, PS_(th) may be set to a value of100, for example, requiring that counter C reach or exceed a value of100 to detect a post-stimulation seizure event and/or to deliversubsequent stimulation therapy. If an increment value of 1 is chosen forstep 2450, for example, it may be possible for the post-stimulationdetection counter C to reach a value of 100 in less than a half-second,assuming a sample rate of 250 samples per second. Of course, thesevalues could be adjusted to meet the needs of a particular patient, orthe requirements of a particular physician. Upon completion of anyadditional stimulation therapy delivery, the post-stimulation detectionprocess may begin once again.

As shown in FIG. 20, post-stimulation data U_(n) continues to beacquired and evaluated after incrementing 2450 (or decrementing 2452)the post-stimulation counter, unless a post-stimulation seizure event isidentified. FIG. 20 also shows that the level cutoff 2446 used forcomparing to the incoming data U_(n) may be determined from a long-termrepresentation of EEG signal data. In one embodiment, the level cutoffmay be a function of a long-term component of the normal seizuredetection algorithm. In certain embodiments, a long-term representationof EEG signal data that is at least partially (and in some cases,entirely) based on pre-stimulation EEG signal values may be used todetermine the level cutoff 2446. In certain further embodiments, thelong-term representation of EEG signal data may be updated toincorporate both pre-stimulation values and newly acquiredpost-stimulation values. In a preferred embodiment, the long-termrepresentation may comprise a recent value of a background signal, BG,determined prior to stimulation (or perhaps a somewhat earlier value ofBG), and updated to include newly acquired post-stimulation data values,wherein BG is determined substantially as described above. In analternate embodiment, the long-term representation may comprise the last(e.g., most recent) value of a long-term average, LTA, determined priorto stimulation (or perhaps a somewhat earlier value of LTA), and updatedto include post-stimulation data values. LTA may be determined using acounter substantially as described above.

In further embodiments, the value 2460 may be multiplied by a scalefactor, k, shown at 2462, to obtain the level cutoff 2446. The scalefactor k may be adjustable and need not be the same as that used by thedetection logic of the normal seizure detection algorithm.

In certain further embodiments, a “floor” value, C_(FL), may be set tolimit how low the post-stimulation detection counter, C, may decrement,according to certain embodiments. This is shown at step 2464. Forexample, if the value of C would fall below C_(FL) as a result ofdecrementing C, then C is set equal to the floor, C_(FL):

If C _(n-1)−(decrement)_(PS) ≦C _(FL), then C _(n) =C _(FL).

FIGS. 21( a) and 21(b) show time plots of EEG signal data correspondingto simulated post-stimulation seizure events, along with the value of apost-stimulation detection counter, C, derived from the respective EEGsignals in accordance with embodiments of the invention. In bothexamples, the post-stimulation detection counter, C, increases and couldbe programmed (e.g., by setting the post-stimulation threshold, PS_(th),to an appropriate value) to detect post-stimulation seizure activity(and hence, deliver subsequent therapy) more quickly than by relying onthe normal seizure detection algorithm.

Thus, a METHOD AND APPARATUS FOR DETECTION OF EPILEPTIC SEIZURES hasbeen provided. While at least one exemplary embodiment has beenpresented in the foregoing detailed description of the invention, itshould be appreciated that a vast number of variations exist. It shouldalso be appreciated that the exemplary embodiment or exemplaryembodiments are only examples, and are not intended to limit the scope,applicability, or configuration of the invention in any way. Rather, theforegoing detailed description will provide those skilled in the artwith a convenient road map for implementing an exemplary embodiment ofthe invention, it being understood that various changes may be made inthe function and arrangement of elements described in an exemplaryembodiment without departing from the scope of the invention as setforth in the appended claims and their legal equivalents.

1. A method of detecting a precursor to a neurological event, the methodcomprising: sampling an electroencephalograph (EEG) signal to obtain astream of data values; counting changes in data value amplitude betweenat least a first amplitude range and a second amplitude range; anddetecting a precursor to a neurological event when the change countexceeds a predetermined precursor threshold.
 2. The method of claim 1wherein one of the amplitude ranges is adapted to identify epileptiformdischarges.
 3. The method of claim 1 further comprising a thirdamplitude range, wherein one of the amplitude ranges is adapted toidentify positive epileptiform discharges, and one of the amplituderanges is adapted to identify negative epileptiform discharges.
 4. Amethod of detecting a precursor to a neurological event, the methodcomprising: sampling an electroencephalograph (EEG) signal to obtain astream of data values; applying the data values to a level transformhaving a level threshold defining two amplitude ranges, the leveltransform adapted to produce a stream of output values, each outputvalue identifying the amplitude range corresponding to each data value;applying the stream of output values to a change detector, the changedetector adapted to produce a stream of change signal values, a changesignal value having a first value if a current output value is differentfrom an immediately preceding output value, and having a second value ifthe current value is the same as the immediately preceding value;counting the first values that occur in a predefined window; anddetecting a precursor to a neurological event when the count of firstvalues exceeds a predetermined precursor threshold.
 5. The method ofclaim 4 wherein the first value is 1, and the second value is 0, andwherein counting the first values comprises computing a sum of thechange signal values in a predefined window.
 6. The method of claim 5wherein the change signal values have a third value greater than 1 ifthe current output value is different from an immediately precedingoutput value by more than a predetermined amount.
 7. The method of claim4 wherein counting the first values in a predefined window comprises:computing a block sum of N change signal values; computing a rolling sumof M block sums to form a precursor trend count; and detecting aprecursor to a neurological event when the precursor trend count exceedsa predetermined precursor threshold.
 8. The method of claim 7 whereinsuccessive block sums are determined using non-overlapping blocks of Nchange signal values.
 9. The method of claim 8 wherein the rolling sumis formed from M consecutive block sum values.
 10. The method of claim 4wherein the neurological event is a seizure.
 11. The method of claim 4where the level threshold is a multiple of a long-term representation ofthe EEG signal.
 12. The method of claim 11 wherein the long-termrepresentation is a background level signal.
 13. The method of claim 4wherein the level transform has two or more level thresholds defining atleast 3 amplitude ranges.
 14. The method of claim 13 wherein the atleast 3 amplitude ranges include a high, a medium, and a low amplituderange.
 15. The method of claim 13 wherein the at least 3 amplituderanges include a high positive, a middle, and a high negative amplituderange.
 16. The method of claim 15 wherein the stream of output valuesproduced by the level transform comprises: a positive 1 for each datavalue corresponding to the high positive amplitude range, a 0 for eachdata value corresponding to the middle amplitude range, and a negative 1for each data value corresponding to the high negative amplitude range.17. A computer-readable medium programmed with instructions forperforming a method of detecting a precursor to a neurological event,the medium comprising instructions for causing a programmable processorto: sample an electroencephalograph (EEG) signal to obtain a stream ofdata values; count changes in data value amplitude between at least afirst amplitude range and a second amplitude range, the amplitude rangesbeing chosen to differentiate between normal EEG signal amplitudes andepileptiform discharges; and detect a precursor to a neurological eventwhen the change count exceeds a predetermined precursor threshold.
 18. Acomputer-readable medium programmed with instructions for performing amethod of detecting a precursor to a neurological event, the mediumcomprising instructions for causing a programmable processor to: samplean electroencephalograph (EEG) signal to obtain a stream of data values;apply the data values to a level transform having a level threshold, thelevel threshold defining two amplitude ranges, the level transformadapted to produce a stream of output values, each output valueidentifying the amplitude range corresponding to each data value; applythe stream of output values to a change detector, the change detectoradapted to produce a stream of change signal values, a change signalvalue having a first value if a current output value is different froman immediately preceding output value, and having a second value if thecurrent value is the same as the immediately preceding value; count thefirst values that occur in a predefined window; and detect a precursorto a neurological event when the count of first values exceeds apredetermined precursor threshold.
 19. The medium of claim 18 whereinthe first value is 1, and the second value is 0, and further comprisinginstructions to count the first values by computing a sum of the changesignal values in a predefined window.
 20. The medium of claim 18 furthercomprising instructions to compute a block sum of N change signalvalues; compute a rolling sum of M block sums to form a precursor trendcount; and detect a precursor to a neurological event when the precursortrend count exceeds a predetermined precursor threshold.
 21. A systemfor detecting a precursor to a neurological event, comprising: animplantable lead adapted to sense EEG signals from a brain of a patient;and an implantable medical device (IMD) in communication with theimplantable lead and adapted to receive EEG signals, the IMD having apower source, memory, and electronic circuitry adapted to sample anelectroencephalograph (EEG) signal to obtain a stream of data values;apply the data values to a level transform having a level threshold, thelevel threshold defining two amplitude ranges, the level transformadapted to produce a stream of output values, each output valueidentifying the amplitude range corresponding to each data value; applythe stream of output values to a change detector, the change detectoradapted to produce a stream of change signal values, a change signalvalue having a first value if a current output value is different froman immediately preceding output value, and having a second value if thecurrent value is the same as the immediately preceding value; count thefirst values that occur in a predefined window; and detect a precursorto a neurological event when the count of first values exceeds apredetermined precursor threshold.
 22. The system claim 21 wherein theIMD is adapted to deliver a therapy when a precursor to a neurologicalevent has been detected.
 23. The system of claim 21 wherein the IMD isadapted to deliver electrical stimulation therapy via the implantablelead.
 24. The system of claim 21 wherein the IMD is adapted to chargestimulation circuitry when a precursor to a neurological event has beendetected.